Intelligent product page automatic generation method and system for the big health industry

Abstract

The present application relates to the field of data processing technology, and discloses a method and system for automatically generating intelligent product pages for the big health industry. The method includes: performing multimodal analysis and augmented reality processing on prototype diagrams and multi-source medical data of the big health industry to obtain enhanced design information; performing dynamic knowledge graph mapping and causal reasoning on the enhanced design information to obtain semantic structure and knowledge association data; performing adaptive microservice deployment analysis on the semantic structure and knowledge association data to obtain containerized deployment strategy and perform intent-driven API design and homomorphic encryption enhancement processing to obtain API interface specification data; performing component design and code generation processing on the API interface specification data to obtain multimodal interactive page code; performing intelligent rendering and dynamic content filling on the multimodal interactive page code to obtain the target product page. The present application improves the efficiency and accuracy of the automatic generation of intelligent product pages for the big health industry.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for automatically generating intelligent product pages for the health industry. Background Art

[0002] In the current healthcare industry, product page development often relies on traditional manual design and coding methods. This approach typically involves UI / UX designers creating prototypes, front-end developers writing HTML, CSS, and JavaScript based on the designs, and back-end developers building APIs and database structures. This process also involves multiple rounds of communication and revisions to ensure the final product meets design and functional requirements. Furthermore, healthcare product pages also require consideration of unique requirements, such as medical data security, privacy protection, and the presentation of complex medical terminology.

[0003] However, this traditional approach has several drawbacks. First, the entire development process is time-consuming and requires the collaboration of multiple professional teams, increasing communication costs and the error rate. Second, manual coding is prone to human error, especially when dealing with complex medical data and logic. Third, traditional methods struggle to adapt quickly to evolving medical needs and technological advancements. Finally, ensuring the security and privacy of medical data often requires additional development work and expertise, increasing development difficulty and cost. Summary of the Invention

[0004] The present application provides a method and system for automatically generating intelligent product pages for the big health industry, which is used to improve the efficiency and accuracy of automatically generating intelligent product pages for the big health industry.

[0005] In the first aspect, the present application provides a method for automatically generating intelligent product pages for the big health industry, and the method for automatically generating intelligent product pages for the big health industry includes: performing multimodal analysis and augmented reality processing on the collected big health industry prototype images and multi-source medical data to obtain enhanced design information; performing dynamic knowledge graph mapping and causal reasoning on the enhanced design information to obtain semantic structure and knowledge association data; performing adaptive microservice deployment analysis on the semantic structure and knowledge association data to obtain a containerized deployment strategy; performing intent-driven API design and homomorphic encryption enhancement processing on the containerized deployment strategy to obtain API interface specification data; performing component design and code generation processing on the API interface specification data to obtain multimodal interaction page code; performing intelligent rendering and dynamic content filling on the multimodal interaction page code to obtain a target product page.

[0006] In combination with the first aspect, in a first implementation method of the first aspect of the present application, the collected big health industry prototype images and multi-source medical data are subjected to multimodal analysis and augmented reality processing to obtain enhanced design information, including: performing image preprocessing on the big health industry prototype images to obtain standardized image data, and performing data cleaning and format unification processing on the multi-source medical data to obtain standardized medical data; performing feature extraction on the standardized image data through a convolutional neural network to obtain an image feature vector, and performing text feature extraction on the standardized medical data to obtain a text feature vector; performing feature fusion on the image feature vector and the text feature vector to obtain fused feature data; performing three-dimensional spatial mapping on the fused feature data to obtain a three-dimensional enhanced design model; performing semantic segmentation on the three-dimensional enhanced design model to obtain hierarchical design structure data; performing geometric transformation and lighting rendering on the hierarchical design structure data to obtain enhanced design information.

[0007] In combination with the first aspect, in the second implementation method of the first aspect of the present application, the enhanced design information is dynamically mapped to a knowledge graph and subjected to causal reasoning to obtain semantic structure and knowledge association data, including: performing named entity recognition on the enhanced design information to obtain a medical entity set, and performing entity relationship extraction on the medical entity set to obtain an entity relationship network; performing knowledge graph construction on the entity relationship network to obtain an initial knowledge graph, and performing knowledge reasoning and supplementation on the initial knowledge graph to obtain a complete knowledge graph; performing low-dimensional vector representation on the complete knowledge graph to obtain a graph embedding vector, and performing semantic similarity calculation on the graph embedding vector to obtain a semantic association matrix; performing causal reasoning on the semantic association matrix to obtain a causal relationship graph, and performing critical path identification on the causal relationship graph to obtain a critical causal chain; performing semantic structure analysis on the critical causal chain to obtain a semantic structure tree, and performing domain ontology alignment on the semantic structure tree to obtain a semantic ontology network; performing multi-source knowledge integration on the semantic ontology network through a knowledge fusion algorithm to obtain semantic structure and knowledge association data.

[0008] In combination with the first aspect, in the third implementation method of the first aspect of the present application, the adaptive microservice deployment analysis of the semantic structure and knowledge-related data to obtain a containerized deployment strategy includes: dividing the semantic structure and knowledge-related data into functional modules to obtain a microservice candidate set, and performing service dependency analysis on the microservice candidate set to obtain a service dependency graph; estimating the service load on the service dependency graph to obtain a service load distribution, and allocating computing resources on the service load distribution to obtain a preliminary resource configuration plan; designing service containerization for the preliminary resource configuration plan to obtain a container combination plan, and planning the service network for the container combination plan to obtain a service network topology structure; designing a service registration and discovery mechanism for the service network topology structure to obtain a service registry, and formulating a traffic distribution strategy for the service registry to obtain load balancing rules; designing a dynamic expansion and contraction strategy for the load balancing rules to obtain an elastic scaling plan, and designing a high availability assurance mechanism for the elastic scaling plan to obtain a fault recovery strategy; generating an automated deployment script for the fault recovery strategy to obtain a containerized deployment strategy.

[0009] In combination with the first aspect, in the fourth implementation method of the first aspect of the present application, the containerized deployment strategy is subjected to intent-driven API design and homomorphic encryption enhancement processing to obtain API interface specification data, including: performing service function analysis on the containerized deployment strategy to obtain a service function list, and performing user intent identification on the service function list to obtain an intent-driven requirement list; performing API endpoint design on the intent-driven requirement list to obtain an API endpoint sketch, and performing request method definition on the API endpoint sketch to obtain an HTTP method mapping table; performing request parameter design on the HTTP method mapping table to obtain an input parameter pattern, and performing response structure design on the input parameter pattern to obtain an output data format; performing data flow analysis on the output data format to obtain sensitive data tags, and performing homomorphic encryption scheme design on the sensitive data tags to obtain an encrypted field mapping; performing key management mechanism design on the encrypted field mapping to obtain a key distribution strategy, and formulating a secure communication protocol for the key distribution strategy to obtain an encrypted transmission scheme; generating API documents for the encrypted transmission scheme to obtain API interface specification data.

[0010] In combination with the first aspect, in the fifth implementation of the first aspect of the present application, the component design and code generation processing of the API interface specification data to obtain the multimodal interactive page code includes: performing interface layout analysis on the API interface specification data to obtain the page structure framework, and dividing the page structure framework into components to obtain a component hierarchy tree; defining component attributes on the component hierarchy tree to obtain a component configuration list, and performing state management design on the component configuration list to obtain a state flow diagram; performing event processing mechanism design on the state flow diagram to obtain an event listener list, and performing data processing on the event listener list. According to the binding rule formulation, a data synchronization solution is obtained; the API call logic design is performed on the data synchronization solution to obtain an interface call flowchart, and an error handling strategy is formulated for the interface call flowchart to obtain an exception handling mechanism; a multimodal interaction design is performed on the exception handling mechanism to obtain an interaction mode definition, and the interaction mode definition is integrated with barrier-free functions to obtain an accessibility solution; code template matching is performed on the accessibility solution to obtain a basic code structure, and component logic injection is performed on the basic code structure to obtain candidate component code, and the candidate component code is integrated to obtain the multimodal interaction page code.

[0011] In combination with the first aspect, in the sixth implementation method of the first aspect of the present application, the multimodal interactive page code is intelligently rendered and dynamically filled with content to obtain the target product page, including: constructing a DOM tree for the multimodal interactive page code to obtain a page structure tree, and performing style calculation on the page structure tree to obtain a style rule set; performing layout calculation on the style rule set to obtain layout information, and performing layer analysis on the layout information to obtain a rendering hierarchy table; performing drawing operations on the rendering hierarchy table to obtain graphics drawing instructions, and synthesizing the graphics drawing instructions to obtain a page view; binding the page view to a data source to obtain a data binding mapping, and formulating an asynchronous loading strategy for the data binding mapping to obtain a content loading scheme; registering interactive events for the content loading scheme to obtain an event listener set, and designing a state update mechanism for the event listener set to obtain a state manager; setting performance monitoring points for the state manager to obtain a performance indicator set, and integrating the performance indicator set with the page view to obtain the target product page.

[0012] In a second aspect, the present application provides an intelligent product page automatic generation system for the big health industry, the intelligent product page automatic generation system for the big health industry comprising:

[0013] The parsing module is used to perform multimodal parsing and augmented reality processing on the collected big health industry prototype images and multi-source medical data to obtain enhanced design information;

[0014] A mapping module, configured to perform dynamic knowledge graph mapping and causal reasoning on the enhanced design information to obtain semantic structure and knowledge association data;

[0015] A deployment module, configured to perform adaptive microservice deployment analysis on the semantic structure and knowledge association data to obtain a containerized deployment strategy;

[0016] An encryption module is used to perform intent-driven API design and homomorphic encryption enhancement processing on the containerized deployment strategy to obtain API interface specification data;

[0017] A generation module is used to perform component design and code generation processing on the API interface specification data to obtain multimodal interaction page code;

[0018] The filling module is used to intelligently render and dynamically fill the multimodal interactive page code to obtain a target product page.

[0019] The technical solution provided in this application utilizes multimodal parsing and augmented reality processing technologies to accurately understand and transform prototype images and multi-source medical data, significantly improving the integrity and accuracy of design information and laying a solid foundation for subsequent page generation. Dynamic knowledge graph mapping and causal reasoning techniques enable a deep understanding of the complex semantic structures and knowledge relationships within the medical field. This not only ensures the professionalism and accuracy of generated pages but also enables intelligent content display and user interaction. Furthermore, the introduction of adaptive microservice deployment analysis makes the generated pages more scalable and flexible, enabling them to better adapt to the rapidly changing needs of the healthcare industry. Furthermore, intent-driven API design and enhanced homomorphic encryption significantly improve the system's security and privacy protection capabilities, which are crucial for processing sensitive medical data. During component design and code generation, this method automatically generates multimodal interactive page code that meets modern front-end development standards, improving development efficiency and ensuring consistent code quality. Through intelligent rendering and dynamic content filling, high-performance page display and smooth interaction are achieved, significantly enhancing the user experience. This significantly shortens the development cycle, reduces human resource investment, and lowers development costs. It enables rapid response to changing market demands, making product iteration more agile. At the same time, since the entire process is automated, human errors are greatly reduced, the stability of product quality is improved, and the efficiency and accuracy of automatic generation of intelligent product pages for the big health industry are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a schematic diagram of an embodiment of a method for automatically generating an intelligent product page for the health industry in an embodiment of the present application;

[0022] Figure 2 This is a schematic diagram of an embodiment of an intelligent product page automatic generation system for the big health industry in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The embodiments of the present application provide a method and system for automatically generating intelligent product pages for the big health industry. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, an embodiment of the method for automatically generating an intelligent product page for the health industry includes:

[0025] Step S101: Perform multimodal analysis and augmented reality processing on the collected big health industry prototype images and multi-source medical data to obtain enhanced design information;

[0026] It is understandable that the execution subject of this application can be an intelligent product page automatic generation system for the health industry, or a terminal or server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0027] Specifically, input is received from various sources, including prototype images for the healthcare industry (possibly in PNG, JPG, or SVG formats) and various forms of medical data (such as medical records, medical images, and laboratory test results). The prototype images are then preprocessed using advanced image processing techniques, including denoising, contrast adjustment, and resolution normalization, to ensure the accuracy of subsequent analysis. Furthermore, multi-source medical data undergoes data cleaning and format standardization to remove outliers and redundant information, and convert data in different formats into a standard format. Deep learning algorithms, such as convolutional neural networks (CNNs), are then applied to extract features from the preprocessed prototype images, identifying key information such as page layout, UI elements, and design style. For text-based medical data, natural language processing (NLP) techniques, such as BERT or GPT models, are used to extract keywords, semantic structures, and contextual information. For medical image data, specialized medical image analysis algorithms may be used for feature extraction. After feature extraction from each modality, multimodal fusion algorithms, such as attention mechanisms or graph neural networks, are used to integrate information from different sources into a unified feature representation. This fusion process not only considers the characteristics of each modality but also analyzes their interrelationships and dependencies. Next, the fused feature information is mapped into three-dimensional space to create a preliminary augmented reality (AR) model. This AR model visually demonstrates the connection between the prototype design and the medical data. Subsequently, the AR model undergoes further semantic segmentation and structuring to identify different functional areas and interactive elements. Finally, this structured AR model is rendered and optimized, adding appropriate lighting and interactive features to produce the final augmented design information. For example, consider a prototype image of an electronic medical record and some real medical record data. First, the prototype image is analyzed to identify key areas such as the medical record entry area, diagnosis recommendation area, and medication information area. Next, common fields and data structures in the real medical record data are analyzed. During the multimodal fusion stage, the UI elements in the prototype image are matched to the structure of the actual medical record data. This may reveal that some necessary fields are missing from the prototype design, or that the presentation of some fields is not intuitive enough. During the creation of the AR model, the page layout may be automatically adjusted, missing fields may be added, or the data presentation may be optimized. For example, it may change the original flat display of drug information to a three-dimensional drug model, so that doctors and patients can understand the medication information more intuitively.

[0028] Step S102: Perform dynamic knowledge graph mapping and causal reasoning on the enhanced design information to obtain semantic structure and knowledge association data;

[0029] Specifically, the augmented design information is deeply parsed, using named entity recognition (NER) technology to identify key medical concepts, terms, and entities within the text. These entities may include disease names, symptoms, medications, treatments, and more. Furthermore, the system uses relationship extraction algorithms to identify various relationships between these entities, such as "treat," "cause," and "prevent." Next, the system dynamically maps these identified entities and relationships to a pre-built medical domain knowledge graph. This process not only maps the identified entities and relationships to the existing knowledge graph but also dynamically expands and updates the knowledge graph based on new information. For example, if the system identifies a new association between a symptom and a disease that doesn't already exist in the existing knowledge graph, the system automatically adds this new association. After knowledge graph mapping, the system uses graph embedding technologies, such as TransE or ComplEx, to convert the entities and relationships in the knowledge graph into low-dimensional vector representations. This vector representation captures the semantic similarity between entities and relationships, laying the foundation for subsequent causal reasoning. Next, the system applies causal discovery algorithms, such as PC or FCI, to analyze these vector representations and identify potential causal relationships. This process considers not only direct associations but also indirect, multi-step causal chains. For example, the system may discover that a certain lifestyle habit indirectly increases the risk of a certain disease by affecting a certain physiological indicator. After identifying these causal relationships, the system conducts further semantic structure analysis, integrating them into a larger semantic network. This network not only contains entities and relationships, but also includes the hierarchical structure, semantic categories, and contextual information between them. Finally, the system aligns this semantic network with the domain ontology to ensure that the generated knowledge structure is consistent with standard medical terminology and concept systems. Through this complex series of processes, the system ultimately produces a rich, structured dataset of semantic structures and knowledge associations. For example, suppose the enhanced design information includes a page design for diabetes management. The system first identifies key entities such as "diabetes," "blood sugar," "insulin," "exercise," and "diet," as well as the relationships between them, such as "exercise helps control blood sugar." The system then maps this information into the existing medical knowledge graph, potentially discovering new, less widely understood associations, such as a particular form of exercise being particularly effective for controlling blood sugar. When performing causal reasoning, the system might identify a causal chain: irregular sleep and rest habits → endocrine disorders → abnormal insulin secretion → poor blood sugar control → increased risk of diabetic complications. Based on these findings, the system generates a semantic structure encompassing these entities, relationships, and causal chains. This structure not only reflects the information in the page design but also encompasses broader medical knowledge and causal relationships.

[0030] Step S103: Perform adaptive microservice deployment analysis on the semantic structure and knowledge association data to obtain a containerized deployment strategy;

[0031] Specifically, the system conducts an in-depth analysis of semantic structures and knowledge-related data to identify different functional modules and data dependencies. During this process, the system considers factors such as data type, scale, access frequency, and processing complexity to preliminarily delineate potential microservice boundaries. Next, the system applies domain-driven design (DDD) principles to further refine and adjust these preliminarily delineated modules, ensuring that each microservice has clear business boundaries and independent data models. Furthermore, the system analyzes the call relationships and data flows between these microservices to construct a detailed service dependency graph. This dependency graph not only includes direct service calls but also considers indirect data dependencies and potential performance bottlenecks. Subsequently, the system uses a load forecasting algorithm to estimate the resource requirements of each microservice based on historical data and projected business growth. This estimation process considers multiple metrics, such as CPU utilization, memory consumption, and network bandwidth, providing a basis for subsequent resource allocation. Based on these analysis results, the system uses an intelligent resource allocation algorithm to develop a preliminary resource allocation plan for each microservice. This plan considers factors such as service importance, performance requirements, and available resources, striving to maximize resource utilization while ensuring performance. Next, the system enters the containerization design phase. It selects an appropriate base image for each microservice, configures necessary environment variables and dependencies, and generates a Dockerfile. The system also considers service characteristics, such as the need for persistent storage and special networking requirements, to determine whether to use a specific container orchestration strategy. During this process, the system also designs a service discovery mechanism, potentially using tools like Consul or etcd for dynamic service registration and discovery. Furthermore, the system develops a load balancing strategy, potentially using reverse proxy tools like Nginx or Traefik for balanced traffic distribution. Given the unique needs of the healthcare industry, the system also pays special attention to data security and privacy. It may configure additional security measures for services handling sensitive medical data, such as encrypted transmission and access control. Furthermore, the system designs fault recovery and data backup strategies to ensure rapid service restoration and protect critical data in the event of an emergency. Finally, the system generates detailed deployment scripts and configuration files, including Docker Compose files or Kubernetes deployment manifests, to automate the deployment and management of the entire microservice architecture. For example, consider a healthcare platform that includes user management, health data collection, data analysis, and diagnostic recommendations. The system first divides these functions into independent microservices, such as user services, data collection services, analysis services, and diagnostic services. Then, the system analyzes the dependencies between these services, for example, the diagnostic service depends on the output of the data analysis service.During load forecasting, the system may discover that data analysis services require higher computing resources, while user services require larger storage resources. Based on these analyses, the system will configure high-performance containers for data analysis services, possibly using GPU acceleration, and containers with persistent storage for user services. In containerized design, the system will create a dedicated Docker image for each service and set appropriate resource limits and requests in the Kubernetes deployment manifest. For data security, the system may deploy an encryption agent between the diagnostic service and the data storage to ensure the secure transmission of sensitive medical data. Ultimately, the deployment strategy generated by the system includes not only the container configuration for each service, but also network policies, security policies, monitoring and log collection configurations, etc., forming a complete, secure, and efficient microservice deployment solution.

[0032] Step S104: Perform intent-driven API design and homomorphic encryption enhancement processing on the containerized deployment strategy to obtain API interface specification data;

[0033] Specifically, the system first uses the semantic structure and knowledge association data obtained in step S102 to analyze core business concepts and their relationships, laying the foundation for the API resource model. Next, the system analyzes the user interface elements and interaction flows in the prototype diagram, applying natural language processing techniques to identify user operation intent. This may involve using intent classification algorithms such as BERT or deep learning models like XLNet. The system maps these intents to specific API operations, such as GET, POST, and PUT. The system then uses the knowledge graph constructed in step S102 to enrich the semantic content of the API and automatically generates API endpoints for associated resources based on the relationships between entities. The system also applies causal reasoning results to design more intelligent APIs, such as predictive or suggestive API endpoints based on identified key causal chains.

[0034] Based on the above analysis, the system automatically generates a preliminary API endpoint design, including resource paths, HTTP methods, request parameters, and response structures, ensuring compliance with RESTful principles. Next, the system performs data flow analysis, referencing data attribute tags in the knowledge graph to identify sensitive data fields involved in the API. For the identified sensitive data, the system designs a homomorphic encryption scheme, potentially choosing either partially homomorphic encryption (PHE) or fully homomorphic encryption (FHE), depending on the nature of the data and computational requirements. The system also designs a key management mechanism, including key generation, distribution, storage, and rotation strategies, to ensure the security and availability of the encryption system. During the API design process, the system automatically generates security-related endpoints, such as user authentication, authorization, and audit logs, to ensure compliance with the latest security best practices. Finally, the system generates detailed API documentation, including a description of each endpoint, parameter descriptions, request examples, and response formats. This documentation not only includes technical details but also includes usage guidelines and best practice recommendations, along with semantic annotations to help developers understand the connection between the API and business domain knowledge.

[0035] For example, suppose you're designing an API for a smart health management platform. The system first analyzes the semantic structure, identifying core concepts such as "user," "health data," and "exercise plan." By analyzing the prototype diagram, the system identifies key operational intents, such as "recording health data," "generating personalized exercise recommendations," and "viewing health trends."

[0036] Based on these analyses, the system designs API endpoints such as:

[0037] 1.POST / health-data: records the user's health data;

[0038] GET / exercise-recommendations: Get personalized exercise recommendations.

[0039] 3.GET / health-trends: View health trend analysis.

[0040] Leveraging the knowledge graph, the system automatically adds relevant endpoints, such as GET / nutrition-info / {food-id}, to provide nutritional information related to exercise recommendations. Applying causal reasoning, the system designed a predictive API endpoint: GET / health-risk-prediction, which predicts potential health risks based on the user's health data and lifestyle. During data stream analysis, the system identified that health data contains sensitive information. Therefore, for the POST / health-data endpoint, the system uses partially homomorphic encryption to encrypt specific health indicators, while fully homomorphic encryption is used for numerical health data that may require statistical analysis. The API documentation not only provides technical details for these endpoints, but also includes semantic annotations. For example, for the / health-risk-prediction endpoint, the documentation explains the causal relationships on which the prediction is based and how to interpret the prediction results, helping developers better understand and use the API.

[0041] Step S105: Component design and code generation processing are performed on the API interface specification data to obtain multimodal interaction page code;

[0042] Specifically, the system conducts an in-depth analysis of the API interface specification data to extract key data structures, operation types, and business logic. Based on this information, the system designs a preliminary page layout and component structure, taking into account user experience principles and the unique needs of the big health industry. Next, the system utilizes a component-based design approach to break the page into multiple independent but interrelated components. Each component is assigned specific functions and responsibilities, such as data display, user input, and interaction control. During this phase, the system also considers component reusability and maintainability, potentially creating common component libraries to improve development efficiency. Subsequently, the system defines detailed properties and events for each component, based on the API interface specification to ensure that the components can correctly interact with backend services. The system also designs inter-component communication mechanisms, potentially employing state management patterns such as Flux or Redux to handle complex data flows and state changes. Next, the system enters the code generation phase. It selects appropriate front-end frameworks and libraries, such as React, Vue, or Angular, and uses these tools' best practices to generate component code. During the generation process, the system automatically handles key logic such as API calls, data binding, and state management. For components that handle encrypted data, the system integrates the necessary decryption logic to ensure secure data display and processing on the client side. Given the unique characteristics of the healthcare industry, the system also pays special attention to generating code that complies with accessibility standards (such as WCAG) to ensure that the generated pages are user-friendly for all user groups. Furthermore, the system generates necessary error handling and exception handling code to enhance application robustness. Regarding multimodal interaction design, the system automatically selects the appropriate interaction mode based on the API's functionality and data type. For example, for numerical health data, the system might generate a chart or dashboard component; for image data, an image viewer with zooming and annotation support; and for time series data, an interactive timeline component. The system also considers different input methods, such as touch, voice, or gesture input, and generates corresponding processing code. Finally, the system generates the overall page framework code, assembling all components and adding the necessary routing logic and global state management. Furthermore, the system generates unit and integration test code to ensure the quality of the generated page code. Let's use a concrete example to illustrate this process: suppose you are designing a page for a remote patient monitoring system. The API specification defines interfaces for accessing basic patient information, real-time vital signs, and historical health records. The system first designs a page layout that includes a patient information card, a real-time monitoring panel, and historical data charts. It then divides this page into multiple components. For the VitalSignsMonitor component, the system generates logic for real-time data updates, potentially using WebSocket technology for real-time communication.For the HealthHistoryChart component, the system generates an interactive time series chart, allowing medical staff to select different time ranges and health indicators. Taking into account the possible need to use it on mobile devices, the system also generates responsive layout code for these components. When processing sensitive data, such as patients' personal information, the system generates client-side decryption code to ensure that the data is always encrypted during transmission and storage. The system also generates code for the voice control interface, allowing medical staff to quickly view information about different patients or adjust monitoring parameters through voice commands. The final generated multimodal interactive page code is not only fully functional, but also has good performance, security and accessibility, and can meet the complex needs of remote patient monitoring systems.

[0043] Step S106: Intelligently render and dynamically fill the multimodal interactive page code to obtain the target product page.

[0044] Specifically, the system parses the code for multimodal interactive pages to construct a complete Document Object Model (DOM) tree structure. During this process, the system identifies the various components and elements on the page and understands their hierarchical relationships and interaction logic. Next, the system performs style calculations, applying CSS rules to each node in the DOM tree to determine the specific style properties for each element. This stage may involve complex CSS selector matching and style inheritance calculations, and the system optimizes this process to improve rendering efficiency. The system then performs layout calculations to determine the precise position and size of each element on the page. During this process, the system considers responsive design requirements and dynamically adjusts the layout based on different device screen sizes and orientations. For complex layouts such as Flexbox or Grid layouts, the system uses efficient algorithms to accurately calculate the position of each element. After completing the layout calculations, the system performs layer analysis to divide the page elements into different rendering layers. This step is critical for optimizing rendering performance, especially for elements that contain animations or frequently update. The system intelligently determines which elements require separate layers to minimize repaint and reflow overhead. Next, the system performs the actual drawing, converting each layer into a bitmap. During this process, the system considers various rendering optimization techniques, such as hardware acceleration and caching strategies, to improve drawing efficiency and reduce resource consumption. For complex visual effects such as gradients, shadows, or filters, the system uses GPU acceleration to speed up rendering. After drawing is complete, the system composites the layers, overlaying them in the correct order to form the final page view. This phase also handles advanced visual effects such as transparency and blending modes. The system then enters the dynamic content ingestion phase. It identifies the data sections of the page that need to be dynamically loaded and retrieves the corresponding data from the backend API based on predefined data binding rules. During this process, the system intelligently manages data requests, potentially using caching, preloading, or lazy loading strategies to optimize data loading performance. For large amounts of data or complex data structures, the system may employ virtual scrolling or paging to ensure page responsiveness. During the data ingestion process, the system also handles data formatting, localization, and internationalization requirements to ensure the content is presented in a manner that best suits the user. For data that needs to be updated in real time, the system will establish a WebSocket connection or use long polling technology to achieve real-time data synchronization. The system will also register necessary event listeners to handle user interactions and dynamic updates. This includes handling form submissions, button clicks, scroll events, etc., as well as managing complex state changes and communication between components. Finally, the system will perform performance optimization and monitoring. It will set key performance indicator monitoring points, such as First Contentful Paint (FCP), Maximum Contentful Paint (LCP), Cumulative Layout Shift (CLS), etc., and collect these indicators in real time.Based on these metrics, the system dynamically adjusts rendering strategies, such as adjusting resource loading order and optimizing the critical rendering path, to provide the best user experience. For example, imagine rendering a complex medical dashboard page. This page includes components such as a patient list, real-time vital sign monitoring, a medical image viewer, and a treatment plan timeline. The system first constructs the DOM tree, calculates styles, and performs responsive layout calculations to ensure the page displays correctly on different devices. During the layer analysis phase, the system places the live-updating vital sign monitoring component in a separate layer to optimize update performance. During the drawing phase, the system uses WebGL to render complex medical images, providing high-performance zooming and panning capabilities. During the data population phase, the system first loads key patient information and then uses WebSockets to retrieve real-time vital sign data. For large amounts of historical data, the system uses virtual scrolling to render only the visible area. The system also registers for various interaction events, such as clicking a patient list item to update the detail view or dragging on the treatment plan timeline to adjust the plan. Finally, the system continuously monitors page performance and dynamically adjusts the update strategy to reduce CLS if it detects layout drift caused by vital sign data updates. Through these complex steps, the system finally presents a medical dashboard page with rich functions, superior performance, and smooth interaction, providing medical staff with an efficient work interface.

[0045] In the embodiments of this application, multimodal parsing and augmented reality processing technologies enable accurate understanding and transformation of prototype images and multi-source medical data, significantly improving the integrity and accuracy of design information and laying a solid foundation for subsequent page generation. Dynamic knowledge graph mapping and causal reasoning techniques enable a deep understanding of the complex semantic structures and knowledge relationships in the medical field. This not only ensures the professionalism and accuracy of generated pages but also enables intelligent content display and user interaction. The introduction of adaptive microservice deployment analysis makes the generated pages more scalable and flexible, better adapting to the rapidly changing needs of the healthcare industry. Specifically, adaptive microservice deployment allows page functionality to be modularized, with each microservice independently developed, deployed, and expanded. This architecture enables pages to dynamically load different functional modules based on demand without requiring a complete page rebuild. For example, when adding a new health data visualization feature, only the corresponding microservice needs to be developed and deployed, then integrated into the existing page without affecting other functions. This modular and loosely coupled nature significantly improves page scalability. The microservice architecture also provides better fault isolation capabilities. Even if a problem occurs in a functional module, it will not affect the operation of the entire page, thereby enhancing the stability and reliability of the system. At the same time, intent-driven API design and homomorphic encryption enhancements have greatly improved the system's security and privacy protection capabilities, which are crucial for processing sensitive medical data. In the component design and code generation stages, this method can automatically generate multimodal interactive page code that meets modern front-end development standards, which not only improves development efficiency but also ensures consistency in code quality. Through intelligent rendering and dynamic content filling, high-performance page display and smooth interaction are achieved, greatly improving the user experience. The development cycle is significantly shortened, human resource investment is reduced, and development costs are reduced. It can quickly respond to changes in market demand, making product iteration more agile. At the same time, since the entire process is automated, human errors are greatly reduced, the stability of product quality is improved, and the efficiency and accuracy of automatic generation of intelligent product pages for the big health industry are improved.

[0046] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0047] (1) Perform image preprocessing on the prototype images of the big health industry to obtain standardized image data, and perform data cleaning and format unification on multi-source medical data to obtain standardized medical data;

[0048] (2) Perform feature extraction on the standardized image data using a convolutional neural network to obtain an image feature vector, and perform text feature extraction on the standardized medical data to obtain a text feature vector;

[0049] (3) Perform feature fusion on the image feature vector and the text feature vector to obtain fused feature data;

[0050] (4) Performing three-dimensional spatial mapping on the fused feature data to obtain a three-dimensional enhanced design model;

[0051] (5) Perform semantic segmentation on the 3D enhanced design model to obtain hierarchical design structure data;

[0052] (6) Perform geometric transformation and lighting rendering on the hierarchical design structure data to obtain enhanced design information.

[0053] Specifically, image preprocessing for prototype images in the big health industry involves multiple steps, including denoising, contrast adjustment, color correction, and resolution standardization. This process may use a Gaussian filter to remove noise, then apply histogram equalization to enhance contrast, followed by color space conversion to ensure color consistency, and finally adjust the image to a uniform resolution. At the same time, the cleaning and format unification of multi-source medical data may involve steps such as missing value processing, outlier detection, data type conversion, and format standardization. For example, it may be necessary to convert medical record data from different sources into a unified JSON format, or convert various medical images into a standard DICOM format.

[0054] Feature extraction from standardized image data typically uses pre-trained convolutional neural networks, such as ResNet or VGG networks. These networks can effectively capture high-level image features, such as shape, texture, and layout. Meanwhile, text feature extraction from standardized medical data may utilize natural language processing techniques, such as word embedding or the more advanced BERT model, to capture semantic information in text data.

[0055] The extracted image feature vector and text feature vector are fused. This process may use an attention mechanism or a multimodal fusion network to fully utilize the complementary information between different modal data. The fused feature data contains comprehensive information of the prototype image and medical data, providing a rich basis for subsequent processing. Next, the fused feature data is mapped into a three-dimensional space. This step may use dimensionality reduction techniques such as t-SNE or UMAP, combined with a three-dimensional modeling algorithm to convert abstract feature information into a specific three-dimensional structure. This three-dimensional enhanced design model can intuitively display the relationship and structure between the prototype design and medical data. Among them, the implementation process of three-dimensional space mapping of the fused feature data to obtain a three-dimensional enhanced design model is as follows:

[0056] First, the fused feature data is normalized. This step ensures that features of different dimensions have the same scale, preventing certain features from dominating the final mapping results due to their large numerical range. Standardization can be performed using the Z-score method, which is to subtract the mean of each feature and divide it by the standard deviation. The t-SNE (t-distributed stochastic neighbor embedding) algorithm is used to reduce the high-dimensional fused features to a three-dimensional space. The principle of t-SNE is to maintain the local relationship between data points while revealing the global structure of the data. It achieves dimensionality reduction by minimizing the KL divergence between the probability distribution of data points in high-dimensional space and the probability distribution of corresponding points in low-dimensional space.

[0057] The specific steps include:

[0058] a) Calculate the conditional probability between data points in high-dimensional space to represent the similarity between points.

[0059] b) The locations of randomly initialized points in low-dimensional space.

[0060] c) Use the gradient descent method to iteratively optimize the position of the midpoint in the low-dimensional space so that the distribution of the midpoint in the low-dimensional space is as close as possible to the distribution in the high-dimensional space.

[0061] Post-process the output of t-SNE to enhance the structure in the three-dimensional space. This may include using a force-directed algorithm to adjust the position of points so that related elements are closer and unrelated elements are farther apart. Map the original design elements and medical data concepts to points in the three-dimensional space. This step requires establishing a mapping relationship to convert the semantic information in the high-dimensional feature space into attributes such as position, size, and shape in the three-dimensional space. Based on the optimized three-dimensional point distribution, build the actual three-dimensional model. This includes assigning geometric shapes, colors, and textures to each point. Important design elements may be represented as larger objects, while minor elements are represented as smaller objects. Add visual elements that represent the relationships between elements in the three-dimensional model, such as connecting lines, color coding, etc. These relationships are based on the semantic information contained in the original fused features.

[0062] For example, consider the product page design for a smart health monitoring system. The prototype includes the homepage layout, user information panel, health data dashboard, and activity suggestion module. Related medical data includes user health metrics, historical data, and doctor recommendations. After feature fusion, a high-dimensional vector is obtained, which contains both visual design features (such as layout and color scheme) and medical data features (such as data type and importance).

[0063] 1. During data preprocessing, this high-dimensional vector is normalized to ensure that features of different nature, such as "blood pressure value" and "page color saturation", have the same weight in subsequent processing.

[0064] 2. During the dimensionality reduction phase, the t-SNE algorithm attempts to find an arrangement in three-dimensional space so that similar elements (for example, all design elements and data related to heart rate) are close to each other in three-dimensional space.

[0065] During the spatial structure optimization phase, you might find that the user information panel and the health data dashboard are too close together in 3D space. Using a force-directed algorithm, you can slightly separate these two elements while maintaining their connection to their respective related elements.

[0066] 4. During the semantic mapping phase, you might decide to use spheres to represent data points, where the size of the sphere represents the importance of the data and the color represents the data type (e.g., red represents a warning message).

[0067] 5. During the 3D modeling phase, corresponding 3D objects are created for each design element. For example, the homepage might be represented as a large plane, while the health data dashboard might be a series of small chart objects floating on this plane.

[0068] 6. Finally, in the relationship visualization stage, a glowing line might be added between the user information panel and the health data dashboard to indicate the data flow between them. Meanwhile, the activity suggestion module might be connected to the relevant health indicators with a dotted line to indicate that the recommendations are generated based on these indicators.

[0069] Transforming the original flat design prototype into a layered and meaningful three-dimensional model not only intuitively displays the page structure but also expresses the connections and importance of data through spatial relationships and visual elements. This three-dimensional representation provides a foundation for subsequent augmented reality applications and offers designers new perspectives for optimizing the structure and functionality of product pages.

[0070] Semantic segmentation of 3D augmented design models is a key step, dividing the model into regions or components with specific meanings. This process may use 3D convolutional neural networks or point cloud segmentation algorithms to accurately segment the model based on its geometric features and semantic information. The segmentation results in hierarchical design structure data, which clearly demonstrates the hierarchy and organization of the design. Finally, the system performs geometric transformation and lighting rendering on the hierarchical design structure data. Geometric transformations may include operations such as rotation, scaling, or deformation to optimize the visual effects and functionality of the design. Lighting rendering uses advanced rendering algorithms, such as ray tracing or global illumination, to create realistic visual effects and enhance the three-dimensionality and realism of the design.

[0071] For example, imagine designing the user interface for a telemedicine platform. The prototype might be a complex layout that includes a patient information display, a video call interface, and an electronic medical record system. The system first pre-processes the prototype, standardizing its resolution and color scheme. It also cleans and unifies the format of patient data from different hospitals. Then, using a convolutional neural network, the system extracts key visual elements from the prototype, such as button placement and information block division. For textual data, such as medical terminology and diagnosis descriptions, the system uses an NLP model to extract key concepts and semantic relationships. Next, the system fuses these features to create a comprehensive representation, which is then mapped into three-dimensional space to form an interactive 3D interface model. Using semantic segmentation, the system identifies distinct functional areas, such as patient information, video call, and medical record areas. Finally, the system optimizes and renders the 3D model, perhaps adding a soft lighting effect to emphasize important information or using a specific color scheme to distinguish different functional areas. The resulting enhanced design not only retains the core elements of the original design but also incorporates the unique characteristics of medical data, resulting in a visually appealing and functional telemedicine platform interface design.

[0072] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0073] (1) Perform named entity recognition on the enhanced design information to obtain a medical entity set, and then perform entity relationship extraction on the medical entity set to obtain an entity relationship network;

[0074] (2) Construct a knowledge graph for the entity relationship network to obtain an initial knowledge graph, and then perform knowledge reasoning and supplementation on the initial knowledge graph to obtain a complete knowledge graph;

[0075] (3) Perform a low-dimensional vector representation on the complete knowledge graph to obtain the graph embedding vector, and perform semantic similarity calculation on the graph embedding vector to obtain the semantic association matrix;

[0076] (4) Perform causal reasoning on the semantic association matrix to obtain a causal relationship graph, and identify the key path of the causal relationship graph to obtain the key causal chain;

[0077] (5) Perform semantic structural analysis on the key causal chain to obtain a semantic structure tree, and align the semantic structure tree with domain ontology to obtain a semantic ontology network;

[0078] (6) The semantic ontology network is integrated with multi-source knowledge through the knowledge fusion algorithm to obtain semantic structure and knowledge association data.

[0079] Specifically, named entity recognition (NER) for augmented design information is fundamental to the entire process. This step typically utilizes pre-trained deep learning models, such as BERT or BiLSTM-CRF. These models can accurately identify medical-related entities in text, such as disease names, symptoms, medications, and treatments. After identification, the system classifies and standardizes these medical entities to form a structured set of medical entities. Next, neural network models using distant supervised learning or attention mechanisms are used. These models can automatically identify semantic relationships between entities, such as "treatment," "cause," and "prevention," thereby forming a preliminary entity-relationship network. With this entity-relationship network in place, the system can begin constructing a knowledge graph. This process begins by mapping entities and relationships into a graph structure, with each entity as a node and each relationship as an edge. At this stage, the system may utilize graph database technologies, such as Neo4j, to efficiently store and query this complex relational data. After constructing the initial knowledge graph, the system then performs knowledge reasoning and supplementation. This may involve using rule-based reasoning engines or probabilistic graphical models, such as Markov logic networks, to infer underlying implicit relationships and knowledge. For example, if the graph contains the relationships "Drug A treats Disease B" and "Disease B is caused by Virus C," the system might infer a new relationship, such as "Drug A may be effective against Virus C." Through such reasoning and supplementation, the initial knowledge graph gradually evolves into a more complete and richer knowledge system.

[0080] To better utilize and analyze this complex knowledge graph, the system needs to convert it into a low-dimensional vector representation. This process typically utilizes graph embedding techniques such as TransE, ComplEx, or more advanced graph neural network models. These methods map each entity and relationship in the graph into a low-dimensional continuous vector space, preserving the semantic information between entities and relationships. Once these graph embedding vectors are obtained, the system can efficiently calculate semantic similarity. This typically involves using metrics such as cosine similarity or Euclidean distance to calculate the similarity between different entity or relationship vectors, thereby forming a semantic association matrix. This matrix reveals potential connections between medical concepts and provides an important foundation for subsequent analysis. The system then performs causal inference on the semantic association matrix. This step may combine statistical methods (such as structural equation modeling) with machine learning techniques (such as causal discovery algorithms) to identify possible causal relationships. This process considers not only direct associations but also indirect, multi-step causal chains. Through this analysis, the system constructs a causal graph that illustrates the complex causal network between medical concepts. After generating the causal relationship graph, the system identifies key paths. This may involve using graph algorithms such as the shortest path or PageRank variants to identify the most important or influential causal chains. These key causal chains represent the core medical knowledge and reasoning paths. The concepts and relationships within the causal chains are organized into a hierarchical structure, forming a semantic structure tree. This tree clearly demonstrates the hierarchical relationships and semantic organization between medical concepts. Next, this semantic structure tree is aligned with a predefined medical domain ontology. This step typically involves ontology matching techniques, such as semantic similarity calculation and structure matching algorithms, to align the automatically generated knowledge structure with standardized medical terminology and concept systems. Through this alignment process, the system generates a standardized semantic ontology network that retains new knowledge discovered from the data while maintaining good compatibility with the existing medical knowledge system. Knowledge fusion algorithms are used to integrate multi-source knowledge within the semantic ontology network. This process may involve knowledge graph fusion techniques, such as entity alignment, relationship mapping, and conflict resolution. This approach integrates knowledge from various sources (such as literature data, clinical records, and expert knowledge) with the automatically generated semantic ontology network, resolving potential contradictions and duplications to form a unified, comprehensive knowledge system. The resulting semantic structure and knowledge association data not only encompass rich medical knowledge but also preserve complex semantic relationships and reasoning paths.

[0081] For example, suppose we are processing a medical knowledge base about pneumonia. The system first identifies relevant entities from various medical literature and clinical reports, such as "a certain virus," "ACE2 receptor," and "lung inflammation." It then extracts relationships between these entities, such as "a certain virus enters human cells through the ACE2 receptor." While constructing the knowledge graph, the system may infer new relationships, such as "a certain virus causes lung inflammation" and "lung inflammation may cause breathing difficulties," leading to "a certain viral infection may cause breathing difficulties." During graph embedding, "a certain virus" and "influenza virus" may be close in the vector space because they are both respiratory viruses. In causal reasoning, the system may identify a key causal chain from "viral infection" to "cytokine storm" and then to "multiple organ failure." In semantic structure analysis, "virus," "symptoms," and "complications" may form a hierarchical structure. Finally, in the knowledge fusion stage, the system may integrate this automatically generated knowledge with official guidelines published by the WHO or CDC to form a comprehensive and authoritative pneumonia knowledge base. This knowledge base not only includes the symptoms, transmission routes, diagnostic methods and treatment strategies of the disease, but also covers complex pathological mechanisms and potential research directions, providing valuable knowledge resources for medical workers and researchers.

[0082] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0083] (1) Semantic structure and knowledge association data are divided into functional modules to obtain a microservice candidate set, and inter-service dependency analysis is performed on the microservice candidate set to obtain a service dependency graph;

[0084] (2) Estimate the service load based on the service dependency graph to obtain the service load distribution, and then perform computational resource allocation on the service load distribution to obtain a preliminary resource allocation plan;

[0085] (3) Design service containers for the preliminary resource allocation plan to obtain a container combination plan, and plan the service network for the container combination plan to obtain the service network topology;

[0086] (4) Design a service registration and discovery mechanism for the service network topology to obtain a service registry, and formulate a traffic distribution strategy for the service registry to obtain load balancing rules;

[0087] (5) Design a dynamic expansion and contraction strategy for the load balancing rules to obtain an elastic scaling solution, and design a high availability guarantee mechanism for the elastic scaling solution to obtain a fault recovery strategy;

[0088] (6) Generate an automated deployment script for the fault recovery strategy to obtain a containerized deployment strategy.

[0089] Specifically, functional module partitioning based on semantic structures and knowledge-related data is fundamental to the entire process. This step typically employs a domain-driven design (DDD) approach, combining business requirements and data flow analysis to partition the entire system into multiple independent but interrelated functional modules. Clustering algorithms or graph partitioning techniques may be used to assist in identifying potential service boundaries, while also considering the cohesion and coupling of each module. This partitioning results in a preliminary set of candidate microservices, each representing a relatively independent functional unit within the system. Static code analysis tools and dynamic call chain tracing techniques are used to deeply analyze the call relationships, data flows, and shared resources between services. This analysis enables the construction of a detailed service dependency graph, clearly demonstrating the interaction patterns and interdependencies between microservices. This dependency graph not only encompasses direct service calls but also considers indirect data dependencies and potential performance bottlenecks.

[0090] With a service dependency graph in hand, service load estimation can be performed. This step typically combines historical data analysis with predictive models, such as time series analysis or machine learning algorithms, to estimate the resource requirements of each service under different scenarios. This estimation process considers multiple metrics, such as CPU usage, memory consumption, and network bandwidth, to form a comprehensive service load distribution map. Based on this load distribution, intelligent computing resource allocation is performed, potentially using heuristic algorithms or linear programming methods to optimize resource utilization while ensuring performance, resulting in a preliminary resource allocation plan. Service containerization design is then performed on this preliminary resource allocation plan. This process involves selecting appropriate container technologies (such as Docker), designing container image build strategies, and configuring container networking and storage. A dedicated container definition is created for each microservice, including base image selection, environment variable configuration, and dependency management. This design results in a detailed container composition plan, providing each microservice with an independent and portable runtime environment. Next, service networking is planned for this container composition plan, which may involve using service mesh technologies (such as Istio or Linkerd) to manage inter-service communication. A complete service network topology is designed, defining inter-service communication paths, security policies, and traffic control rules. Once the service network topology is determined, the service registration and discovery mechanism is designed. This typically involves selecting an appropriate service registry (such as Consul or ZooKeeper) and designing service registration, deregistration, and health check policies. This step establishes a dynamically maintained service registry, providing the foundation for efficient inter-service communication. Based on this service registry, a detailed traffic distribution strategy is developed, including load balancing algorithms (such as round-robin, least connections, or weighted response time), traffic distribution rules, and service degradation policies, forming a complete set of load balancing rules. Dynamic scaling strategies are designed for the load balancing rules. This process may utilize automated capacity planning algorithms, combining real-time monitoring data with predefined performance metrics to dynamically adjust the number of service instances. This design results in an elastic scaling solution that automatically adjusts resource allocation based on actual load conditions. Based on this elastic scaling solution, high availability mechanisms are also designed, including multi-AZ deployment strategies, data backup and recovery mechanisms, and service circuit breaking and degradation policies. These designs together form a comprehensive fault recovery strategy, ensuring rapid recovery and service continuity in the face of various potential failures.

[0091] Convert all of these designs and strategies into automated deployment scripts. This process might utilize Infrastructure as Code (IaC) tools like Terraform or Ansible to generate detailed deployment configurations and scripts. These scripts include not only container build and deployment instructions, but also network configuration, storage mounts, security policy settings, and more. Through these automated scripts, a complete containerized deployment strategy is ultimately achieved, enabling automated deployment and management of the entire microservices architecture.

[0092] For example, suppose you are designing a large-scale telemedicine platform. During the functional module segmentation phase, you might identify microservices such as user management, appointment booking, video consultation, electronic medical records, and prescription management. When analyzing service dependencies, you might find that the video consultation service depends on user management and appointment booking, while the prescription management service depends on the electronic medical records service. When performing load estimation, you might predict that the video consultation service experiences higher loads in the mornings and evenings on weekdays, while the electronic medical records service might experience peak loads after consultations. Based on this analysis, you might allocate more computing resources to the video consultation service and design an automatic scaling strategy to cope with peak loads. In the containerization design, you might choose a GPU-accelerated container image for the video consultation service to improve video processing performance. In network planning, you might configure a dedicated high-bandwidth network channel for the video consultation service and implement traffic control policies to ensure quality of service. When designing the service registration and discovery mechanism, you might choose Consul as the service registry and configure an automatic service discovery mechanism based on health checks. For load balancing, you might implement a geo-based intelligent routing strategy for the video consultation service to direct user requests to the nearest service node. In the elastic scaling solution, automatic scaling rules based on CPU usage and concurrent connections are set. To ensure high availability, a multi-region deployment strategy may be designed, and real-time data synchronization and fast failover mechanisms may be implemented for critical services such as electronic medical records. Finally, a complete set of Terraform scripts is generated, allowing a single-click deployment of the entire telemedicine platform to the cloud environment, including container deployment for all microservices, network configuration, and security group settings. This design and implementation ensures that the telemedicine platform operates efficiently, securely, and reliably, providing stable services for patients and doctors.

[0093] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0094] (1) Analyze the service functions of the containerized deployment strategy to obtain a service function list, and identify user intent on the service function list to obtain a list of intent-driven requirements;

[0095] (2) Design the API endpoints based on the intent-driven requirement list to obtain an API endpoint sketch, and define the request method for the API endpoint sketch to obtain an HTTP method mapping table;

[0096] (3) Design the request parameters for the HTTP method mapping table to obtain the input parameter pattern, and design the response structure for the input parameter pattern to obtain the output data format;

[0097] (4) Perform data flow analysis on the output data format to obtain sensitive data tags, and design a homomorphic encryption scheme for the sensitive data tags to obtain encrypted field mapping;

[0098] (5) Design a key management mechanism for the encrypted field mapping to obtain a key distribution strategy, and formulate a secure communication protocol for the key distribution strategy to obtain an encrypted transmission scheme;

[0099] (6) Generate API documentation for the encrypted transmission solution to obtain API interface specification data.

[0100] Specifically, the functional definition and data model of each microservice are parsed to form a comprehensive service function list. This list includes key information such as each service's core functions, data processing logic, and input and output types. For example, in a telemedicine platform, the service function list includes functional modules such as patient information management, appointment scheduling, video diagnosis and treatment, electronic medical records, and medication prescriptions. User intent is identified within the service function list, leveraging natural language processing techniques, specifically intent classification algorithms, to analyze potential user operation intentions and usage scenarios. Intent classification algorithms perform semantic analysis of user queries or operation instructions and map them to predefined intent categories. In the example of a telemedicine platform, the user query "How to make an appointment for a cardiology clinic next Wednesday" is identified as the "appointment scheduling" intent. This process generates an intent-driven requirements list that links high-level user needs to specific service functions.

[0101] Based on the intent-driven requirements list, API endpoint design is performed. This step creates corresponding API endpoints for each identified user intent, following RESTful design principles. In the telemedicine platform, a " / appointments" endpoint is designed for the "appointment scheduling" intent. This process results in a rough draft of the API endpoints, which contains the basic structure and functional description of each endpoint. Next, request methods are defined within the API endpoint sketch, determining the HTTP method used by each endpoint. For example, the " / appointments" endpoint defines the POST method for creating new appointments and the GET method for querying appointment information. This step generates an HTTP method mapping table, clarifying the correspondence between API endpoints and HTTP methods. Request parameter design is performed within the HTTP method mapping table, detailing the required input parameters for each API endpoint. In the appointment scheduling API, input parameters include patient ID, doctor ID, appointment date and time, and department. This process generates a complete input parameter schema, providing clear guidance for API usage. Furthermore, the response structure is designed within the input parameter schema, defining the format and structure of the data returned by the API. For example, a successful appointment might return the appointment confirmation number, appointment time, and doctor information. This step also results in a detailed output data format specification.

[0102] Subsequently, data flow analysis was performed on the output data format to trace the data's flow through the system and identify how sensitive information is processed and transmitted. In the telemedicine platform, patient personal information and medical records are marked as sensitive data. This analysis process resulted in sensitive data markings, identifying data fields requiring special security handling. A homomorphic encryption scheme was designed for these sensitive data markings, allowing necessary data processing to be performed while encrypted. For example, partially homomorphic encryption was used for patient age data, enabling the system to perform age statistics analysis without decrypting the data. This design process generated an encrypted field mapping, specifying which fields use which homomorphic encryption algorithm. A key management mechanism was designed for the encrypted field mapping, including strategies for key generation, distribution, storage, and update. A hierarchical key management strategy was implemented in the telemedicine platform, assigning different keys to different levels of data access. This design resulted in a detailed key distribution strategy, specifying how encryption keys are securely distributed to authorized system components and users. Based on this key distribution strategy, a secure communication protocol was developed to ensure appropriate protection during all data transmission processes. This protocol defines key elements such as the encryption algorithm, key exchange mechanism, and session management, forming a complete encrypted transmission solution.

[0103] Finally, API documentation is generated for the entire encrypted transmission solution. This documentation not only contains technical details for each API endpoint, but also includes important information such as secure usage guidelines and recommendations for handling encrypted data. In the example of a telemedicine platform, the API documentation details how to securely call the appointment API, including how to properly handle encrypted patient information and how to verify server identity to prevent man-in-the-middle attacks. This process ultimately produces comprehensive API interface specifications, providing developers with detailed implementation guidance.

[0104] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0105] (1) Analyze the interface layout of the API interface specification data to obtain the page structure framework, and divide the page structure framework into components to obtain a component hierarchy tree;

[0106] (2) Define component attributes for the component hierarchy tree to obtain a component configuration list, and perform state management design on the component configuration list to obtain a state flow diagram;

[0107] (3) Design an event processing mechanism for the state flow diagram to obtain an event listener list, and formulate data binding rules for the event listener list to obtain a data synchronization solution;

[0108] (4) Design the API call logic for the data synchronization solution to obtain an interface call flow chart, and formulate an error handling strategy for the interface call flow chart to obtain an exception handling mechanism;

[0109] (5) Conduct multimodal interaction design for the exception handling mechanism to obtain the interaction mode definition, and integrate the accessibility function into the interaction mode definition to obtain the accessibility solution;

[0110] (6) Code template matching is performed on the accessibility solution to obtain the basic code structure, and component logic is injected into the basic code structure to obtain candidate component codes. The candidate component codes are integrated to obtain the multimodal interaction page code.

[0111] Specifically, layout analysis algorithms, such as constraint-based layout analysis or heuristic layout reasoning, are used to parse the API's structure and data flow, thereby deriving the basic structural framework of the page. For example, in a telemedicine consultation system, the API interface specification data includes key data points such as patient information, doctor list, and appointment time slots. The layout analysis algorithm maps these data points to corresponding page blocks, forming a preliminary page structure framework.

[0112] The page structure framework is divided into components. This step uses component recognition algorithms, such as pattern matching-based component recognition or deep learning-based UI component classification, to decompose the page structure into independent functional components. In the example of a telemedicine consultation system, the page might be divided into components such as a header navigation, a patient information form, a doctor selection list, and an appointment calendar. This process generates a component hierarchy tree that clearly displays the hierarchical relationships and nested structure between components.

[0113] Defining component attributes for the component hierarchy is the next important step. Using an attribute inference algorithm, we define the attributes and behaviors for each component based on the data types and business rules in the API specification. For example, a doctor selection list component might be defined to include attributes such as doctor name, specialty, and rating, as well as a click-to-select behavior. This process produces a detailed component configuration list containing all the attributes and methods for each component. Subsequently, state management is designed for the component configuration list, using state machine modeling techniques to define state transfer and data flow between components, resulting in a complete state flow diagram.

[0114] The state flow diagram provides the foundation for designing the event handling mechanism. This step uses the event-driven programming model to define the corresponding processing logic for each possible user interaction and system event, generating a comprehensive list of event listeners. In a telemedicine system, this includes handling events such as a user selecting a doctor and submitting an appointment request. Next, data binding rules are defined for this list of event listeners. Using data flow analysis algorithms, we determine how each event triggers data updates and propagation, ultimately creating a complete data synchronization solution.

[0115] The data synchronization solution lays the foundation for the design of API call logic. This step uses workflow modeling techniques to design the API call sequence required for each user operation, forming a detailed interface call flow chart. For example, when a user submits an appointment request, the system sequentially calls APIs to verify user identity, check physician availability, and create an appointment record. Subsequently, an error handling strategy is developed for the interface call flow chart. Using exception handling design patterns, processing logic is defined for each possible error scenario, resulting in a comprehensive exception handling mechanism. Designing multimodal interaction for this exception handling mechanism is a key step in improving the user experience. Here, applying multimodal interaction theory, corresponding interface responses are designed for different types of interaction (such as touch, voice, and gesture), forming a complete interaction pattern definition. In a telemedicine system, this might include supporting voice commands for booking appointments or gestures for browsing physician information. Next, accessibility features are integrated into the interaction pattern definition, using accessible design principles to ensure the interface is user-friendly for individuals with special needs, such as the visually and hearing-impaired, resulting in a comprehensive accessibility solution.

[0116] Finally, code template matching is performed on the accessibility solution. Using a template matching algorithm, the most suitable template is selected from a predefined code template library to generate the basic code structure. Component logic is then injected into this basic code structure. Using code generation techniques, the previously designed component logic, event handling, data binding, and other functions are converted into actual code implementations to generate candidate component code. The final step is to integrate the candidate component code. Using a code assembly algorithm, all component codes are combined into a complete, fully functional multimodal interactive page code.

[0117] For example, during the automatic page generation process for a remote heart monitoring system, the API interface specification data includes endpoints such as basic patient information, real-time heart rate data, and historical ECG records. Interface layout analysis maps these data points into a structural framework consisting of a patient information bar at the top, a real-time heart rate display area in the center, and a history list at the bottom. Component partitioning identifies components such as the patient information component, the real-time heart rate chart component, and the history table component. During the component property definition phase, the real-time heart rate chart component is assigned properties such as the data source, update frequency, and alarm threshold. State management design defines how to trigger an alarm state and affect the display of other components when the heart rate exceeds the alarm threshold. The event handling mechanism designs how to expand detailed information when a user clicks on a history record and how to handle real-time data update events. Data binding rules ensure that real-time heart rate data is reflected in the chart in a timely manner without affecting system performance. The API call logic designs how to periodically retrieve the latest heart rate data from the backend and how to retrieve detailed history records upon user request. Error handling strategies consider handling situations such as network outages and data anomalies. Multimodal interaction design allows doctors to switch between different patient data displays using voice commands or zoom in on ECG details using gestures. Accessibility features ensure that color-blind users can clearly discern changes in the heart rate graph. The resulting page code is not only fully functional but also highly usable and accessible, providing strong technical support for remote heart disease monitoring.

[0118] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0119] (1) Constructing a DOM tree for the multimodal interactive page code to obtain a page structure tree, and performing style calculation on the page structure tree to obtain a style rule set;

[0120] (2) Perform layout calculation on the style rule set to obtain layout information, and perform layer analysis on the layout information to obtain a rendering level table;

[0121] (3) performing a drawing operation on the rendering level table to obtain a graphics drawing instruction, and synthesizing the graphics drawing instruction to obtain a page view;

[0122] (4) Bind the data source to the page view to obtain a data binding map, and formulate an asynchronous loading strategy for the data binding map to obtain a content loading solution;

[0123] (5) Register interactive events for the content loading scheme to obtain an event listener set, and design a state update mechanism for the event listener set to obtain a state manager;

[0124] (6) Setting performance monitoring points for the state manager to obtain a performance indicator set, and integrating the performance indicator set with the page view to obtain the target product page.

[0125] Specifically, a recursive descent parsing algorithm is used to convert HTML code into a tree structure. The time complexity of DOM tree construction can be expressed as follows:

[0126]

[0127] Where n is the total number of HTML elements, α i and β i are the analytical and interpolation coefficients of the ith element, d i is the depth of the i-th element.

[0128] Perform style calculation on the page structure tree, using the selector matching algorithm and cascading rules to apply CSS styles to the corresponding DOM nodes. The complexity of style calculation can be expressed as:

[0129]

[0130] Where p is the number of DOM nodes, m is the number of CSS rules, k is the maximum complexity of the selector, γ ij and δ ij are the matching and application coefficients of the i-th node to the j-th rule, λ ij is the selector complexity index.

[0131] Perform layout calculations on the style rule set, using the box model algorithm and the flow layout algorithm to calculate the exact position and size of each element. The time complexity of the layout calculation can be expressed as:

[0132]

[0133] Where e is the number of elements in the page, r i is the relative position complexity of the i-th element, ∈ i and ζ i They are the layout calculation and position adjustment coefficients of the elements respectively.

[0134] The layout information is analyzed layer by layer, using z-index sorting and a compositing layer identification algorithm to generate a rendering hierarchy table. Drawing operations are then performed on the rendering hierarchy table, using a vector drawing algorithm to convert the layout and style information into a series of drawing instructions. The graphics drawing instructions are then composited, using a layer compositing algorithm to overlay all layers in the correct order to produce the final page view.

[0135] Bind the page view to the data source and use data binding algorithms, such as dirty checking or virtual DOM comparison, to associate dynamic data with page elements. Then, develop an asynchronous loading strategy for the data binding mapping, using lazy loading and preloading algorithms to optimize data loading performance. Register interactive events for the content loading solution, using event delegation and event bubbling mechanisms to add necessary event listeners to page elements. Design a state update mechanism for the event listener collection and use state management patterns to design a unified state manager. Set performance monitoring points for the state manager and use performance indicator collection algorithms to obtain a set of key performance indicators. Integrate these performance indicators with the page view and use real-time performance optimization algorithms to dynamically adjust page rendering and interaction strategies, ultimately obtaining the optimized target product page.

[0136] For example, in the automatic page generation process of an intelligent chronic disease management system, a DOM tree containing key nodes such as the patient health data dashboard, medication reminders, and lifestyle recommendations is first constructed. Assuming that the page contains 500 HTML elements with an average depth of 5, the DOM tree construction time is approximately:

[0137]

[0138] During the style calculation phase, specific medical interface design rules are applied, such as using a high-contrast color scheme to accommodate visually impaired patients. Assuming there are 300 CSS rules and a maximum selector complexity of 4, the style calculation time is approximately:

[0139]

[0140] During the layout calculation phase, each element is precisely positioned, such as placing important health indicators at the top of the page. Assuming there are 200 elements on a page that require layout calculation, and the average relative position complexity is 2, the layout calculation time is approximately:

[0141]

[0142] During the data binding phase, real-time blood sugar levels, blood pressure data, and other metrics are associated with display components. An asynchronous loading strategy ensures real-time updates of critical health data, while lazy loading is used to load non-urgent historical data. Event registration adds event listeners for key actions (such as the emergency help button), and the state manager design ensures that all relevant components can respond to state changes promptly when abnormal health indicators are detected.

[0143] Finally, performance monitoring includes monitoring points for key metrics such as First Contentful Paint (FCP), Largest Contentful Paint (LCP), and Cumulative Layout Shift (CLS). By adjusting rendering strategies in real time, such as prioritizing the loading of critical health data during poor network conditions, the page ensures efficient operation under various conditions. This intelligent page generation approach not only improves the development efficiency of the chronic disease management system, but also ensures high performance and reliability in complex medical scenarios, providing patients with better self-management tools.

[0144] The above describes the method for automatically generating an intelligent product page for the health industry in the embodiment of the present application. The following describes the system for automatically generating an intelligent product page for the health industry in the embodiment of the present application. Figure 2 In one embodiment of the present application, an intelligent product page automatic generation system for the health industry includes:

[0145] The parsing module 201 is used to perform multimodal parsing and augmented reality processing on the collected big health industry prototype images and multi-source medical data to obtain enhanced design information;

[0146] A mapping module 202 is configured to perform dynamic knowledge graph mapping and causal reasoning on the enhanced design information to obtain semantic structure and knowledge association data;

[0147] A deployment module 203 is configured to perform adaptive microservice deployment analysis on the semantic structure and knowledge association data to obtain a containerized deployment strategy;

[0148] An encryption module 204 is used to perform intent-driven API design and homomorphic encryption enhancement processing on the containerized deployment strategy to obtain API interface specification data;

[0149] A generation module 205 is used to perform component design and code generation processing on the API interface specification data to obtain multimodal interaction page code;

[0150] The filling module 206 is used to perform intelligent rendering and dynamic content filling on the multimodal interactive page code to obtain a target product page.

[0151] Through the collaborative efforts of these components, multimodal parsing and augmented reality processing technologies enable accurate understanding and transformation of prototype images and multi-source medical data, significantly improving the completeness and accuracy of design information and laying a solid foundation for subsequent page generation. Dynamic knowledge graph mapping and causal reasoning techniques achieve a deep understanding of the complex semantic structures and knowledge relationships in the medical field. This not only ensures the professionalism and accuracy of generated pages but also enables intelligent content display and user interaction. Furthermore, the introduction of adaptive microservice deployment analysis makes the generated pages more scalable and flexible, better adapting to the rapidly changing needs of the healthcare industry. Furthermore, intent-driven API design and enhanced homomorphic encryption significantly improve the system's security and privacy protection capabilities, which are crucial for processing sensitive medical data. During component design and code generation, this method automatically generates multimodal interactive page code that meets modern front-end development standards, improving development efficiency and ensuring consistent code quality. Through intelligent rendering and dynamic content incorporation, high-performance page display and smooth interaction are achieved, significantly enhancing the user experience. This significantly shortens the development cycle, reduces human resources, and lowers development costs. It can quickly respond to changes in market demand, making product iteration more agile. At the same time, because the entire process is automated, it greatly reduces human error, improves product quality stability, and enhances the efficiency and accuracy of automatic generation of intelligent product pages for the healthcare industry.

[0152] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0153] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for automatically generating intelligent product pages for the health industry, characterized in that: The method for automatically generating intelligent product pages for the health industry includes: Perform multimodal analysis and augmented reality processing on the collected big health industry prototype images and multi-source medical data to obtain enhanced design information; Performing dynamic knowledge graph mapping and causal reasoning on the enhanced design information to obtain semantic structure and knowledge association data; Performing adaptive microservice deployment analysis on the semantic structure and knowledge association data to obtain a containerized deployment strategy; Perform intent-driven API design and homomorphic encryption enhancement processing on the containerized deployment strategy to obtain API interface specification data; Component design and code generation processing are performed on the API interface specification data to obtain multimodal interactive page code, including: performing interface layout analysis on the API interface specification data to obtain a page structure framework, and dividing the page structure framework into components to obtain a component hierarchy tree; defining component attributes on the component hierarchy tree to obtain a component configuration list, and performing state management design on the component configuration list to obtain a state flow diagram; designing an event handling mechanism on the state flow diagram to obtain an event listener list, and formulating data binding rules for the event listener list to obtain a data synchronization solution; performing API call logic design on the data synchronization solution to obtain an interface call flow chart, and formulating an error handling strategy for the interface call flow chart to obtain an exception handling mechanism; performing multimodal interaction design on the exception handling mechanism to obtain an interaction mode definition, and integrating barrier-free functions on the interaction mode definition to obtain an accessibility solution; performing code template matching on the accessibility solution to obtain a basic code structure, and performing component logic injection on the basic code structure to obtain candidate component code, and integrating the candidate component code to obtain the multimodal interactive page code; The multimodal interactive page code is intelligently rendered and dynamically filled with content to obtain a target product page.

2. The method for automatically generating intelligent product pages for the health industry according to claim 1, characterized in that: The multimodal analysis and augmented reality processing of the collected big health industry prototype images and multi-source medical data are performed to obtain enhanced design information, including: Performing image preprocessing on the prototype image of the big health industry to obtain standardized image data, and performing data cleaning and format unification processing on the multi-source medical data to obtain standardized medical data; Performing feature extraction on the standardized image data using a convolutional neural network to obtain an image feature vector, and performing text feature extraction on the standardized medical data to obtain a text feature vector; Performing feature fusion on the image feature vector and the text feature vector to obtain fused feature data; Performing three-dimensional spatial mapping on the fused feature data to obtain a three-dimensional enhanced design model; performing semantic segmentation on the three-dimensional enhanced design model to obtain hierarchical design structure data; The hierarchical design structure data is subjected to geometric transformation and lighting rendering to obtain enhanced design information.

3. The method for automatically generating intelligent product pages for the health industry according to claim 1, characterized in that: The dynamic knowledge graph mapping and causal reasoning of the enhanced design information to obtain semantic structure and knowledge association data includes: Performing named entity recognition on the enhanced design information to obtain a medical entity set, and performing entity relationship extraction on the medical entity set to obtain an entity relationship network; Constructing a knowledge graph for the entity relationship network to obtain an initial knowledge graph, and performing knowledge reasoning and supplementation on the initial knowledge graph to obtain a complete knowledge graph; Performing a low-dimensional vector representation on the complete knowledge graph to obtain a graph embedding vector, and performing semantic similarity calculation on the graph embedding vector to obtain a semantic association matrix; Performing causal relationship reasoning on the semantic association matrix to obtain a causal relationship graph, and performing critical path identification on the causal relationship graph to obtain a critical causal chain; Performing semantic structural analysis on the key causal chain to obtain a semantic structure tree, and performing domain ontology alignment on the semantic structure tree to obtain a semantic ontology network; The semantic ontology network is subjected to multi-source knowledge integration through a knowledge fusion algorithm to obtain semantic structure and knowledge association data.

4. The method for automatically generating intelligent product pages for the health industry according to claim 1, characterized in that: The adaptive microservice deployment analysis of the semantic structure and knowledge association data to obtain a containerized deployment strategy includes: Dividing the semantic structure and knowledge association data into functional modules to obtain a microservice candidate set, and analyzing inter-service dependencies on the microservice candidate set to obtain a service dependency graph; Estimating the service load on the service dependency graph to obtain a service load distribution, and allocating computing resources on the service load distribution to obtain a preliminary resource allocation plan; Performing service containerization design on the preliminary resource configuration plan to obtain a container combination plan, and performing service network planning on the container combination plan to obtain a service network topology structure; Designing a service registration and discovery mechanism for the service network topology to obtain a service registry, and formulating a traffic distribution strategy for the service registry to obtain load balancing rules; Designing a dynamic expansion and contraction strategy for the load balancing rule to obtain an elastic scaling solution, and designing a high availability guarantee mechanism for the elastic scaling solution to obtain a fault recovery strategy; An automated deployment script is generated for the fault recovery strategy to obtain a containerized deployment strategy.

5. The method for automatically generating intelligent product pages for the health industry according to claim 1, characterized in that: The containerized deployment strategy is subjected to intent-driven API design and homomorphic encryption enhancement processing to obtain API interface specification data, including: Performing a service function analysis on the containerized deployment strategy to obtain a service function list, and performing user intent recognition on the service function list to obtain an intent-driven demand list; Designing an API endpoint for the intent-driven requirement list to obtain an API endpoint sketch, and defining a request method for the API endpoint sketch to obtain an HTTP method mapping table; Designing request parameters for the HTTP method mapping table to obtain an input parameter pattern, and designing a response structure for the input parameter pattern to obtain an output data format; Performing data flow analysis on the output data format to obtain sensitive data tags, and designing a homomorphic encryption scheme for the sensitive data tags to obtain an encrypted field mapping; Designing a key management mechanism for the encryption field mapping to obtain a key distribution strategy, and formulating a secure communication protocol for the key distribution strategy to obtain an encryption transmission scheme; Generate API documentation for the encrypted transmission solution to obtain API interface specification data.

6. The method for automatically generating intelligent product pages for the health industry according to claim 1, characterized in that: The intelligent rendering and dynamic content filling of the multimodal interactive page code to obtain the target product page includes: Performing a DOM tree construction on the multimodal interactive page code to obtain a page structure tree, and performing style calculation on the page structure tree to obtain a style rule set; Performing layout calculation on the style rule set to obtain layout information, and performing layer analysis on the layout information to obtain a rendering level table; Performing a drawing operation on the rendering level table to obtain a graphics drawing instruction, and synthesizing the graphics drawing instruction to obtain a page view; Binding the page view to a data source to obtain a data binding map, and formulating an asynchronous loading strategy for the data binding map to obtain a content loading solution; Registering interactive events for the content loading solution to obtain an event listener set, and designing a state update mechanism for the event listener set to obtain a state manager; Performance monitoring points are set for the state manager to obtain a performance indicator set, and the performance indicator set is integrated with the page view to obtain a target product page.

7. An intelligent product page automatic generation system for the health industry, characterized by: Used to execute the method for automatically generating an intelligent product page for the big health industry according to any one of claims 1 to 6, the intelligent product page automatic generation system for the big health industry comprising: The parsing module is used to perform multimodal parsing and augmented reality processing on the collected big health industry prototype images and multi-source medical data to obtain enhanced design information; A mapping module, configured to perform dynamic knowledge graph mapping and causal reasoning on the enhanced design information to obtain semantic structure and knowledge association data; A deployment module, configured to perform adaptive microservice deployment analysis on the semantic structure and knowledge association data to obtain a containerized deployment strategy; An encryption module is used to perform intent-driven API design and homomorphic encryption enhancement processing on the containerized deployment strategy to obtain API interface specification data; The generation module is used to perform component design and code generation processing on the API interface specification data to obtain multimodal interactive page code, including: performing interface layout analysis on the API interface specification data to obtain a page structure framework, and dividing the page structure framework into components to obtain a component hierarchy tree; defining component attributes on the component hierarchy tree to obtain a component configuration list, and performing state management design on the component configuration list to obtain a state flow diagram; performing event processing mechanism design on the state flow diagram to obtain an event listener list, and formulating data binding rules for the event listener list to obtain Data synchronization solution; performing API call logic design on the data synchronization solution to obtain an interface call flow chart, and formulating an error handling strategy for the interface call flow chart to obtain an exception handling mechanism; performing multimodal interaction design on the exception handling mechanism to obtain an interaction mode definition, and integrating accessibility functions on the interaction mode definition to obtain an accessibility solution; performing code template matching on the accessibility solution to obtain a basic code structure, and performing component logic injection on the basic code structure to obtain candidate component code, and integrating the candidate component code to obtain the multimodal interaction page code; The filling module is used to intelligently render and dynamically fill the multimodal interactive page code to obtain a target product page.

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